A dissertation explores deep learning techniques to address challenges in environmental science, aiming for accurate, efficient, and explainable solutions. It introduces WaLeF and FIDLAr for flood prediction and water level management, outperforming traditional models in accuracy and efficiency. The work also presents CoDiCast, a diffusion model for probabilistic weather forecasting, and Hypercube-RAG, an enhanced retrieval-augmented generation system for answering environmental science questions with reduced hallucinations. AI
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IMPACT Introduces novel deep learning models for environmental prediction and knowledge retrieval, potentially improving accuracy and efficiency in climate and disaster management.
RANK_REASON The cluster describes a dissertation presenting novel deep learning models for environmental science problems. [lever_c_demoted from research: ic=1 ai=1.0]